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Found 4 papers, 1 papers with code
Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights
This model achieves better accuracy than previously used convolutional based approaches (ConvNets) optimized with only a continuous loss function.
High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach
In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) is arguably needed to capture the differences in canopy height.
Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies
Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions.
A Machine-learning framework for automatic reference-free quality assessment in MRI
Therefore, the assessment or the ensurance of sufficient image quality in an automated manner is of high interest.
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